The purpose of this study was twofold: to examine the effectiveness of a VSM
intervention to reduce disruptive behaviors in students with ID and to assess whether treatment effects would be maintained at a one-month follow-up. Moreover, as VSM has been used before with this population, positive results would thus support its external validity for reducing
disruptive classroom behaviors. Specific hypotheses were: a) the intervention would reduce disruptive behaviors, and b) effects would be maintained at follow-up. Results showed that meaningful changes were seen in two of the three students after viewing their videos.
Specifically, Student 1 and 2 showed decreases in disruptive behaviors during treatment, with the most notable changes for Student 2. His behaviors evidenced a substantial level change, from 35% during baseline to 7.8% during treatment. Changes for Student 1 were less dramatic, as a reduction during the baseline phase dampened the potential effect of the intervention. Even so, the intervention did bring a clear pattern change as disruptive behaviors decreased to very low levels with little variability. Follow-up observations conducted one-month after the end of treatment demonstrated maintenance of treatment effects for these two students. For Student 2, disruptive behaviors were actually reduced further at follow-up (Student 2 disruptive behaviors at follow-up: M=3.6%). Given the brevity of the videos and treatment phase, this finding is quite remarkable.
As for Student 3, there was a change in level at follow-up, but this cannot be
unambiguously shown to be the effect of the intervention; furthermore, the change was small and especially unimpressive in light of the still high amount of disruptive behaviors present. Thus, an overall evaluation of the effect of the intervention is muddied due to the inadequate treatment phase for Student 3.
Regarding Students 1 and 2, maintenance of treatment effects is conceivably due to transfer of stimulus control. Although environmental variables were not measured, it is possible that appropriate behaviors initially induced through watching the videos were later maintained after coming in contact with teacher praise and attention, for example. A transfer to natural reinforcers makes the changes much more likely to remain as time goes on (Cooper, Heron, &
Heward, 2007).
Recognizing the shortcomings in the data for Student 3 and the speculative nature of any conclusions drawn from it, it is still reasonable to state that the introduction of the intervention did not bring the type of response seen in Student 1 and 2. The behavior of Student 3 appeared unchanged from baseline. There are likely many contributing factors as to why more significant changes were not seen, but the simplest one is that the VSM intervention was not implemented long enough to have the desired effect.
The treatment phase for Student 3 lasted for only two observational sessions. It could be that more significant changes would have been seen had treatment continued. Although the necessary length of treatment needed for VSM to work has not been evaluated, researchers have recommended that students watch their modeling videos multiple times (Shukla-Mehta et al., 2010) over the course of several weeks for optimal results (Bellini & McConnell, 2010; Kehle &
Bray, 2009).
Another potential factor for the lack of response was the behaviors presented in Student 3’s videos. Unlike Student 1 and 2, Student 3’s final videos consisted almost entirely of him engaged in quiet, independent seatwork. There was little variety in the displayed behaviors. The behaviors depicted, although appropriate for certain kinds of tasks (e.g., completing problems in a workbook), were inappropriate for others (e.g., listening to instruction, engaging in a group
activity, giving an answer to a problem). A problem may have been that there were relatively few opportunities to engage in the behaviors depicted in the videos. Quiet, on-task activities were the predominant behaviors shown because the student only rarely engaged in appropriate,
participatory behaviors. It could have been beneficial, although more intrusive to the classroom environment as well as incongruent with the study protocol, to have had the teacher prompt the student for appropriate responses during filming to increase the variety of behaviors captured on video. In this way, the depicted behaviors would be better suited for the range of activities present in the classroom environment.
Student 3’s unique behavioral problems likely also contributed significantly for the lack of response to intervention. While all the behaviors targeted by intervention were disruptive to classroom instruction, Student 3’s behaviors appeared to have a unique function compared to the other students. Although a formal functional behavioral assessment was not conducted,
observations would suggest that the function of many of Student 3’s behaviors was as a direct access to immediate sensory stimuli (Cipani & Schock, 2010), meaning that reinforcement was produced by the behavior itself or was automatic (Vaughn & Michael, 1982). This direct
function differed from Student 1 and 2’s behaviors, which were likely socially mediated through access to attention (peer or adult) or escape from tasks. In this way, Student 1 and 2’s behaviors were similar to the descriptions of the target behaviors successfully reduced in the study by Bilias-Lolis et al. (2012). The students in that study were said to display “disruptive social behavior” which was reduced by VSM (Bilias-Lolis et al., p. 88). None of the students showed stereotyped disruptive behavior to the degree shown by Student 3 in the present investigation.
Student 3 engaged in frequent stereotyped vocalizations and hand-play in front of his face, with or without an audience present. Such behaviors have been known to be especially difficult to
ameliorate, since the individual has unmediated access to the reinforcer (Piazza et al., 2000) and because physically preventing the individual from engaging in the behavior can be difficult or impossible in practice (Ahearn et al., 2007), in addition to causing side effects such as aggressive behavior (Colon et al., 2012). These behaviors can also interfere with acquiring appropriate modeled responses (Young, Krantz, McClannahan, & Poulson, 1994).
In some reports, behavioral strategies in addition to video-modeling have been more successful than using video-modeling alone (Mason et al., 2013; Shukla-Mehta et al., 2010). To compete with such reliable means of stimulation produced by these behaviors, additional strategies relevant to treating stereotypy, for example, response blocking (Ahearn et al., 2007), identification of competing reinforcers (Ahearn et al., 2005; Roberts-Gwinn, 2001), or
manipulation of motivating conditions (Lang et al., 2010) might be needed in conjunction with video-modeling to reduce these behaviors.
With the different treatment effects as well as student characteristics, it is reasonable to ask whether there is a way to predict who will benefit from this intervention. Student 3, for instance, differed from the other two students in age, comorbid disability, type, and severity of disruptive behavior. While it is perhaps intuitive to hypothesize that these factors could predict response to treatment, there is no definitive evidence to support this. In a review of the literature on video-based modeling (i.e., inclusive of both self and others as models), Rayner, Denholm, and Sigafoos (2009) found that there were currently no empirically-evaluated methods for predicting who will benefit from these interventions. Although certain prerequisite skills are frequently reported (e.g., ability to attend to, and imitate depicted behaviors) and may indeed be essential, the authors noted that a reliable measure that could be used across implementers to
predict a response to intervention has not yet been developed (Rayner et al., 2009). Whether a measure could be used to predict response to treatment is thus still an open question.
In conclusion, the present investigation lends support for using VSM to decrease
disruptive classroom behaviors in middle school students with disruptive behaviors despite lack of a third demonstrated treatment effect. The treatment was effective in reducing socially-mediated disruptive behaviors in two of three students. The treatment had little to no effect in reducing stereotyped or self-stimulatory disruptive behaviors in a third student, likely attributable to limitations of the investigation as well as unique characteristics of the student’s behaviors.
Strengths of the video self-modeling intervention were that it is relatively brief, unintrusive to the classroom, simple to create due to the ubiquitousness of video recording and editing software, and well-received by students and teacher.
Limitations
As with every study, this investigation has its limitations. First, an insufficient amount of inter-observer data was taken. Given this, there is less confidence in the reliability of the data had more inter-observer data been collected.
Another limitation was the brief treatment phase for Student 3. Such limited exposure to the intervention limits the claims that can be made about the effect of the intervention on this student’s behavior. Preferably, the treatment phase would have extended further but events outside the researcher’s control prevented this from happening.
An additional limitation was the amount of variability in the baseline for Student 1 when the intervention was implemented. There was a large fluctuation in disruptive behaviors between Sessions 6 and 7, which then stayed consistent over the next two baseline observations. While his baseline appeared to stabilize here, it would have been more ideal to continue his baseline
phase for at least another observational session. Unfortunately, the practical realities of
conducting research in a school setting required that the treatment phase begin for this student in order to complete the study in the available time frame.
Also, there was the threat of participant reaction to the presence of the researcher
(Kazdin, 2011); that is, that participants may have behaved differently after being told about the filming and while the researcher was present in the classroom. Steps were taken to limit this by having the researcher sit in the classroom for a week prior to recording for students to acclimate to his presence. Also, the researcher used a small tablet device to record videos, which has the benefit of being less conspicuous than a video camera. Although students were made aware that they were to be filmed before the start of study, during the recording process, the researcher could have easily appeared to be writing an email or reading an article as recording a video.
Another threat was the lack of control over certain environmental variables. The first was the variety of classroom activities that took place during mathematics instruction. For example, some lessons contained group games, which tended to be much more active and engaging, while other lessons consisted primarily of quiet, independent seatwork. This is simply the reality of any classroom. Fortunately, most class periods were composed of the same two activities (e.g., group problem-solving and independent seatwork), which provided some consistency in instruction.
The second was the ongoing strategies being used by the teacher and paraprofessionals to manage the students’ behavior. No effort was made to control how support staff interacted with the students (e.g., instructions to provide more or less prompts, correction, praise) or to change individual or class-wide contingency plans. The intervention was inserted into the classroom with all the usual supports in place. Ongoing supports and strategies could have had an additive effect to the implemented intervention.
It should be noted while there are always threats to internal validity, the nature of the multiple-baseline design—as in any good research design—reduces them to an extent. So despite threats to internal validity, there is some degree of confidence that the dependent variable
changed as result of implementation of the intervention as opposed to other extraneous variables.
It is common in single-case research for authors to make a disclaimer about their study’s potential lack of external validity or generalizability. Yes, while it is true that the specific findings herein are a function of these three particular students, in a particular classroom, in one particular middle school, questions regarding generalizability are not unique to single-case design. Replication is the only way to determine reliability and generalizability of findings (Branch & Pennypacker, 2013). This is true not only of single-case research designs, but group designs as well (Smith, 2013).
Lastly, there is a practical limitation to this study. Although the equipment and programs needed to record and edit video have become cheaper, more ubiquitous, and easier to use, the amount of time needed to record the videos could pose a hurdle for the typical school
practitioner. One student in particular required multiple class periods to gather enough usable video. In typical practice, this would likely bar its use, and a teacher would more likely utilize different techniques (e.g., utilize feedforward VSM, another peer as model) if recording required too much time.
Future Research
Future research could improve and extend the current study in a number of ways. First, another researcher could replicate the present study, improving on some of its limitations.
Another could extend our knowledge of how treatment effects are maintained by assessing maintaining variables. The present study assumed that variables present in the classroom
environment (e.g., teacher praise, attention, additional free time as a result of work completion) later maintained the appropriate behaviors prompted by the VSM intervention. It would be interesting to examine this, to see whether appropriate behaviors are met with an increase in teacher praise or attention through a descriptive study or by systematically manipulating these variables using an experimental design.
Future research could also examine problem behaviors more specifically to tease out different effects, instead of grouping a number of behaviors into one category. Another study could test whether VSM is more effective at reducing certain functional classes of behaviors compared to others. Breaking down behaviors more specifically could allow a finer-grained analysis of this kind.
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